Are gossipers looked down upon? A norm-based perspective on the relation between gossip and gossiper status.
Bibliographic record
Abstract
While some scholars regard workplace gossip as norm-violating behavior that costs gossipers status, others suggest that gossip clarifies organizational norms and thereby increases gossiper status. Integrating gossip literature with norm research, we develop a model to distinguish positive gossip from negative gossip and theorize their independent and joint effects on gossiper workplace status via peers' perceptions of norm violation and norm clarification-two concurrent but countervailing mechanisms. We hypothesize that positive gossip relates positively to norm clarification perceptions but negatively to norm-violation perceptions, whereas negative gossip relates positively to both norm clarification and norm-violation perceptions. Interactively, positive gossip weakens the norm-violation effects of negative gossip on gossiper status, and each type of gossip replaces the norm clarification effects of the other type of gossip on gossiper status. These hypotheses were largely supported in a 2 × 2 between-subjects experiment with 345 full-time employees (Study 1), a three-wave field survey with data from 192 full-time employees (Study 2), and a round-robin field survey with data from 287 focal employees and 1,075 of their team members embedded in 87 teams (Study 3). Three additional studies reported in the supplementary materials revealed contingencies of the hypotheses: The hypotheses received support with a different experimental manipulation (Study 4), and the hypothesized norm-violation effect of negative gossip was not contingent on gossip content (target's self-serving vs. nonself-serving behavior, Study 5) but gossip intention such that the effect became nonsignificant when gossip intention was group-serving (cf. self-serving, Study 6). (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".